base_model: black-forest-labs/FLUX.1-dev
library_name: gguf
license: other
license_name: flux-1-dev-non-commercial-license
license_link: LICENSE.md
quantized_by: mo137
tags:
- text-to-image
- image-generation
- flux
Flux.1-dev in a few experimental custom formats, mixing tensors in Q8_0, fp16, and fp32. Converted from black-forest-labs' original bf16 weights.
Motivation
Flux's weights were published in bf16. Conversion to fp16 is slightly lossy, but fp32 is lossless. I experimented with mixed tensor formats to see if it would improve quality.
Evaluation
I tried comparing the outputs but I can't say with any certainty if these models are significantly better than pure Q8_0. You're probably better off using Q8_0, but I thought I'll share these – maybe someone will find them useful.
Higher bits per weight (bpw) numbers result in slower computation:
20 s Q8_0
23 s 11.024bpw-txt16.gguf
30 s fp16
37 s 16.422bpw-txt32.gguf
310 s fp32
Update 2024-08-26
Two new files. This time the only tensors in Q8_0 are some or all of:
double_blocks.*.img_mlp.0.weight
double_blocks.*.img_mlp.2.weight
double_blocks.*.txt_mlp.0.weight
double_blocks.*.txt_mlp.2.weight
double_blocks.*.img_mod.lin.weight
double_blocks.*.txt_mod.lin.weight
single_blocks.*.linear1.weight
single_blocks.*.linear2.weight
single_blocks.*.modulation.lin.weight
flux1-dev-Q8_0-fp32-11.763bpw.gguf
This version has all the above layers in Q8_0.flux1-dev-Q8_0-fp32-13.962bpw.gguf
This version preserves first 2 layers of all kinds, and first 4 MLP layers in fp32.flux1-dev-Q8_0-fp32-16.161bpw.gguf
This one, first 4 layers of any kind and first 8 MLP layers in fp32.
In the txt16/32 files, I quantized only these layers to Q8_0, unless they were one-dimensional:
img_mlp.0
img_mlp.2
img_mod.lin
linear1
linear2
modulation.lin
But left all these at fp16 or fp32, respectively:
txt_mlp.0
txt_mlp.2
txt_mod.lin
The resulting bpw number is just an approximation from file size.
This is a direct GGUF conversion of black-forest-labs/FLUX.1-dev
As this is a quantized model not a finetune, all the same restrictions/original license terms still apply.
The model files can be used with the ComfyUI-GGUF custom node.
Place model files in ComfyUI/models/unet - see the GitHub readme for further install instructions.
Please refer to this chart for a basic overview of quantization types.
(Model card mostly copied from city96/FLUX.1-dev-gguf - which contains conventional and useful GGUF files.)